Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques

Energy systems, climate change, and public health are among the primary reasons for moving toward electrification in transportation. Transportation electrification is being promoted worldwide to reduce emissions. As a result, many automakers will soon start making only battery electric vehicles (BEVs). BEV adoption rates are rising in California, mainly due to climate change and air pollution concerns. While great for climate and pollution goals, improperly managed BEV charging can lead to insufficient charging infrastructure and power outages. This study develops a novel Micro Clustering Deep Neural Network (MCDNN), an artificial neural network algorithm that is highly effective at learning BEVs trip and charging data to forecast BEV charging events, information that is essential for electricity load aggregators and utility managers to provide charging stations and electricity capacity effectively. The MCDNN is configured using a robust dataset of trips and charges that occurred in California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for a total of 1570167 vehicle miles traveled. The numerical findings revealed that the proposed MCDNN is more effective than benchmark approaches in this field, such as support vector machine, k nearest neighbors, decision tree, and other neural network-based models in predicting the charging events.Comment: 18 pages,8 figures, 4 table

The Risk and Resilience of Plug-in Vehicles in the Presence of an Imperfect Charging Network

This dissertation aims to explore some of the key barriers to realizing the full emission reduction potential of Plug-in Electric Vehicles (PEVs). Specifically, it explores the tradeoffs between Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) at reducing tailpipe emissions in the presence of an imperfect Electric Vehicle (EV) charging network. BEVs are true Zero Emission Vehicles (ZEVs) since they rely solely on energy from a battery (i.e., a rechargeable energy storage system) for propulsion, emitting no tailpipe emissions. In contrast, PHEVs can propel themselves using a combination of battery and internal combustion engine (ICE) energy. As such, driver behavior that determine the extent to which PHEVs’ electric range is used, have a strong influence on their energy use and emission potential. The first few chapters of this dissertation specifically explore the impact of the interaction between driver behavior and technical vehicle parameters on the energy consumption and Green House Gas (GHG) emissions of PHEVs in order to inform policy about the true emission potential of these low emission vehicles. Chapter 2 presents a study that aims to characterize the engine start activity profiles and emission potential of various PHEV models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of starts. The study ultimately finds that long range PHEVs with high battery capacity such as the Chevrolet Volt are ideal for both curbing start emissions via initializing few engine starts and maximizing fuel displacement. Chapter 3 presents two studies that aim to understand the motivations and implications of driver mode, user-selectable drivetrain configuration setting, usage in PHEVs. In addition to comprehensively defining and classifying various drive modes, the first study examines the motivations for drive mode usage using a survey of over 26,000 PEV drivers in California. The second study quantifies the energy use and emission impacts of drive mode usage using on-road vehicle data from 81 Chevy Volts driven in California. Since BEVs aren’t equipped with ICEs, they are far superior to PHEVs at curbing tailpipe emissions. However, given the vehicles are solely powered by electricity, the adoption and acceptance of BEVs is tightly coupled with the quality of the EV charging infrastructure. As such, the scarcity of reliable and functional EV charging stations presents a significant barrier to the widespread adoption of BEVs. This dissertation aims to complement and expand the limited literature on EV charging reliability by examining the impact of EV charger reliability on BEV driver experience and developing a tool to help charging networks effectively meet impending reliability standards. Chapter 4 presents a study that focuses on understanding the impact of EV charger reliability on driver experience. It uses real-world EV charging data to simulate the level of disruption that would’ve occurred to EV drivers had their successful charging sessions been unsuccessful. Additionally, it quantifies how many charging sessions were actually unsuccessful and qualifies how disruptive those unsuccessful charging sessions were to drivers. By quantifying and qualifying the level of disruption associated with both real and hypothetical charge failures, it finds that EV chargers are not all equally important to EV drivers, highlighting the need for more nuanced charging reliability standards to more effectively meet consumer charging needs. Chapter 5 develops a tool enabling EV charging service providers to swiftly detect charge failures that cannot be detected by standard monitoring protocols. By analyzing habitual charging patterns of EV drivers, the tool identifies unexpected gaps in charger usage, indicating potential charger faults. The tool incorporates two anomaly detection models: a naive probability distribution-based technique and a LSTM for complex pattern modeling. Depending on the tool’s preferred confidence level, CPOs could’ve detected potential charging faults 1.5 to 3 times faster with the naive method and 1.5 to 2.4 times faster with the LSTM method.   The Risk and Resilience of Plug-in Vehicles in the Presence of an Imperfect Charging Networ

Emerging Technology Zero Emission Vehicle Household Travel and Refueling Behavior

Results from this report highlight how alternative fuel vehicles are used based on data collected between 2015 and 2020. Alternative fuel vehicles include plug-in electric vehicles (PEVs), vehicles that are either battery electric vehicles (BEVs) or plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs). This category of vehicle technologies is included in the California Air Resources Board’s Zero Emission Vehicle regulations and is referred to as ZEV in this report. We explore the environmental impacts of driving, charging behavior and infrastructure. In households with ZEVs, the data from surveys, loggers, and interviews indicate that those vehicles are being used extensively. This report, which combined the data collected in two consecutive studies between 2015-2020, includes first and second generation PEVs popular in California between 2011-2018. The BEVs include the first-generation, shortrange Nissan Leaf and the long range BEVs such as the Chevrolet Bolt and Tesla Model S. The PHEVs include short range sedans such as the Toyota Prius Plug-in and longer-range vehicles such as the Toyota Prius Prime, Chevrolet Volt and Chrysler Pacifica. The FCVs include the most popular fuel cell vehicle, the Toyota Mirai

Emerging Technology Zero Emission Vehicle Household Travel and Refueling Behavior

Results from this report highlight how alternative fuel vehicles are used based on data collected between 2015 and 2020. Alternative fuel vehicles include plug-in electric vehicles (PEVs), vehicles that are either battery electric vehicles (BEVs) or plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs). This category of vehicle technologies is included in the California Air Resources Board’s Zero Emission Vehicle regulations and is referred to as ZEV in this report. We explore the environmental impacts of driving, charging behavior and infrastructure. In households with ZEVs, the data from surveys, loggers, and interviews indicate that those vehicles are being used extensively. This report, which combined the data collected in two consecutive studies between 2015-2020, includes first and second generation PEVs popular in California between 2011-2018. The BEVs include the first-generation, shortrange Nissan Leaf and the long range BEVs such as the Chevrolet Bolt and Tesla Model S. The PHEVs include short range sedans such as the Toyota Prius Plug-in and longer-range vehicles such as the Toyota Prius Prime, Chevrolet Volt and Chrysler Pacifica. The FCVs include the most popular fuel cell vehicle, the Toyota Mirai